It is a general assumption that pneumatic muscle-type actuators will play an important role in the development of an assistive rehabilitation robotics system. In the last decade, the development of a pneumatic muscle actuated lower-limb leg orthosis has been rather slow compared to other types of actuated leg orthoses that use AC motors, DC motors, pneumatic cylinders, linear actuators, series elastic actuators (SEA) and brushless servomotors. However, recent years have shown that the interest in this field has grown exponentially, mainly due to the demand for a more compliant and interactive human-robotics system. This paper presents a survey of existing lower-limb leg orthoses for rehabilitation, which implement pneumatic muscle-type actuators, such as McKibben artificial muscles, rubbertuators, air muscles, pneumatic artificial muscles (PAM) or pneumatic muscle actuators (PMA). It reviews all the currently existing lower-limb rehabilitation orthosis systems in terms of comparison and evaluation of the design, as well as the control scheme and strategy, with the aim of clarifying the current and on-going research in the lower-limb robotic rehabilitation field.

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as ‘unknown’, because the system cannot recognize the input of the new pattern.

Recently, robot assisted therapy devices are increasingly used for spinal cord injury (SCI) rehabilitation in assisting handicapped patients to regain their impaired movements. Assistive robotic systems may not be able to cure or fully compensate impairments, but it should be able to assist certain impaired functions and ease movements. In this study, the control system of lower extremity orthosis for the body weight support gait training system which implements pneumatic artificial muscle (PAM) is proposed. The hip and knee joint angles of the gait orthosis system are controlled based on the PAM coordinates information from the simulation. This information provides the contraction data for the mono- and bi-articular PAMs that are arranged as posterior and anterior actuators to simulate the human walking motion. The proposed control system estimates the actuators' contraction as a function of hip and knee joint angles. Based on the contraction model obtained, input pressures for each actuators are measured. The control system are performed at different gait cycles and two PMA settings for the mono- and bi-articular actuators are evaluated in this research. The results showed that the system was able to achieve the maximum muscle moment at the joints, and able to perform the heel contact movement. This explained that the antagonistic mono- and bi-articular actuators worked effectively.

Balance training devices such as wobble board, basu ball and balance cushion are the tool use in balance training exercise programme in order to improve muscle strength and restore posture balance due degeneration of body function or injury. Recently, self-balancing scooter such as Segway and hover board showed a positive effects on rehabilitation. However, it is less known how these devices affect muscle physiological properties. This study aims to to measure ankle muscles activation on difference balance training devices and hover board. Besides, a comparison between these device will be done in order to identify if hover board has a promising feature to be an alternative balance training device. In this research, surface EMG (sEMG) was used to record tibialis anterior and gastrocnemius muscle activities. Seventeen healthy subjects were required to stand on three different types of balance training device such as wobble board, balance cushion, bosu ball and a hover board. They were asked to maintain their standing position on each devices for two minutes. Both time domain and frequency domain analysis were used to identify the features of the EMG signal. Time domain analysis measurement involved average rectified value (ARV) and root mean square (RMS), meanwhile for frequency domain, median frequency (MDF) of the signal were measured. The results shows that, the RMS is differed significantly between the balance training devices (p0.05). Besides, less stable devices increased muscle activity were observed. There is not much difference between hover board and the other devices in term of physiological effects of both tibialis anterior and gastrochemious muscle. It is also suggested that hover board offers a promising feature to be an alternative device for balance training device.

This paper introduces the body weight support gait training system known as the AIRGAIT exoskeleton and delves into the design and evaluation of its leg orthosis control algorithm. The implementation of the mono- and biarticular pneumatic muscle actuators (PMAs) as the actuation system was initiated to generate more power and precisely control the leg orthosis. This research proposes a simple paradigm for controlling the mono- and bi-articular actuator movements cocontractively by introducing a cocontraction model. Three tests were performed. The first test involved control of the orthosis with monoarticular actuators alone without a subject (WO/S); the second involved control of the orthosis with mono- and bi-articular actuators tested WO/S; and the third test involved control of the orthosis with mono- and bi-articular actuators tested with a subject (W/S). Full body weight support (BWS) was implemented in this study during the test W/S as the load supported by the orthosis was at its maximum capacity. This assessment will optimize the control system strategy so that the system operates to its full capacity. The results revealed that the proposed control strategy was able to co-contractively actuate the mono- and bi-articular actuators simultaneously and increase stiffness at both hip and knee joints.

This paper explores empirical modeling of McKibben muscle in characterizing its hysteresis behavior and nonlinearities during quasi-static, quasi-rate, and historic dependencies. The unconventional materials-based actuating system called McKibben muscle has excellent properties of power-to-weight ratio, which could be used in rehabilitation orthosis application for condition monitoring, physical enhancement, and rehabilitation therapy. McKibben muscle is known to exhibit hysteresis behavior and it is rate-dependent (the level of hysteresis depends closely on rate of input excitation frequency). This behavior is undesirable and it must be considered in realizing high precision control application. In this paper, the nonlinearities of McKibben muscle is characterized using empirical modeling with multiple correction functions such as shape irregularity and slenderness. A particle swarm optimization (PSO) method is used to determine the best parametric values of the proposed empirical with modified dynamic friction model. The LabVIEW and MATLAB platforms are used for data analysis, modeling and simulation. The results confirm that this model able to significantly characterize the nonlinearities of McKibben muscle while considering all dependencies.